Information processing device estimating a parameter based on acquired indexes representing an exercise state of a subject, information processing method, and non-transitory recording medium

ABSTRACT

An information processing device including a memory that stores a program; and a processor that executes the program. The processor is configured to acquire, from exercise data representing an exercise state of a subject, exercise parameter information including a plurality of parameters that represent the exercise state of the subject and have a correlation with each other. When an animation representing a motion of the subject based on the acquired exercise parameter information is displayed and then an operation for changing a value of a first parameter of the plurality of parameters is received, the processor generates an animation reflecting at least the first parameter for which the value is changed and a second parameter of the plurality of parameters, a value of the second parameter being changed in conjunction with the value of the first parameter.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Continuation Application of U.S. application Ser.No. 17/468,694, filed Sep. 8, 2021, which claims the benefit of JapanesePatent Application No. 2020-158085, filed on Sep. 23, 2020, the entiredisclosure of all of which is incorporated by reference herein.

FIELD

This application relates generally to an information processing device,an information processing method, and a non-transitory recording medium.

BACKGROUND

In the related art, technology is known in which a wearable device, amotion sensor, or the like worn on the body of a user is used to measurevarious indexes (parameters) that represent the exercise state of theuser. For example, Japanese Unexamined Patent Application PublicationNo. 2018-026149 describes an invention that displays an animation thatrepresents the movement of a user on the basis of various acquiredindexes that represent an exercise state. In the invention described inthe Japanese Unexamined Patent Application Publication No. 2018-026149,when the user manually changes the value of an index, the animation canbe changed in accordance with the changed value.

SUMMARY

An information processing device according to the present disclosureincludes:

at least one processor that executes a program stored in at least onememory; wherein

the at least one processor is configured to

-   -   acquire, as indexes representing an exercise state of a certain        subject, a value of a first parameter, and a value of a second        parameter that is an index that differs from and has correlation        with the first parameter,    -   acquire another first parameter that is the first parameter        having a value that differs from the acquired value of the first        parameter,    -   derive, based on the acquired first parameter, a first reference        value as the second parameter in accordance with a model        generated based on sets of a value of a third parameter as an        input of the model and a value of a fourth parameter as an        output of the model, the third parameter being a same type as        the first parameter, the fourth parameter being a same type as        the second parameter, the value of the third parameter and the        value of the fourth parameter representing an exercise state of        each of a plurality of subjects that is a same type as the        certain subject,    -   derive, based on the acquired another first parameter, a second        reference value as the second parameter in accordance with the        model, and    -   estimate, based on the acquired value of the second parameter,        the first reference value, and the second reference value, the        value of the second parameter that corresponds to the another        first parameter.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of this application can be obtained whenthe following detailed description is considered in conjunction with thefollowing drawings, in which:

FIG. 1 is a drawing illustrating the configuration of an animationgeneration system according to an embodiment;

FIG. 2 is a drawing illustrating an example of information stored in anexercise state storage database;

FIG. 3 is a flowchart of parameter acquisition processing according toan embodiment;

FIG. 4 is a flowchart of animation generation processing according to anembodiment;

FIG. 5 is a drawing illustrating an example of a screen on which agenerated animation is displayed;

FIG. 6 is a flowchart of parameter estimation processing according to anembodiment;

FIG. 7 is a drawing illustrating a distribution of sets of acquiredvalues of speed and pitch, and a model generated from the distribution;

FIG. 8 is a drawing for explaining an example of estimating a parameter;

FIG. 9 is a drawing illustrating a distribution of sets of acquiredvalues of speed and vertical movement, and a model generated from thedistribution; and

FIG. 10 is a drawing illustrating a distribution of sets of acquiredvalues of pitch and vertical movement, and a model generated from thedistribution.

DETAILED DESCRIPTION

Hereinafter, embodiments of the present disclosure are described indetail while referencing the drawings. Note that, in the drawings,identical or corresponding components are denoted with the samereference numerals.

Embodiments

An animation generation system 1000 according to an embodiment of thepresent disclosure is a system that generates an animation representinga motion (form) of a user (subject) that is performing movement/exercisesuch as running or the like. As illustrated in FIG. 1 , the animationgeneration system 1000 includes a data transmission device 100, and ananimation generation device 200. Note that, in reality, a plurality ofthe data transmission device 100 is provided for each subject.

In one example, the data transmission device 100 is a small wearabledevice that is worn near the waist, along the trunk of the subject. Asillustrated in FIG. 1 , the data transmission device 100 includes acontroller 110, a storage 120, a communicator 131, an input device 132,an output device 133, and a sensor 134. The controller 110, the storage120, the communicator 131, the input device 132, the output device 133,and the sensor 134 are connected to each other via a bus line BL. Thedata transmission device 100 sends, to the animation generation device200 via the communicator 131, data expressing the motion of the subjectdetected by the sensor 134.

The controller 110 includes at least one processor. Examples of the atleast one processor include a central processing unit (CPU) or the like.By executing a program stored in the storage 120, the controller 110functions as an exercise data transmitter 111 (described later).

The storage 120 includes at least one memory. Examples of the at leastone memory include a read-only memory (ROM), a random access memory(RAM), a flash memory, or the like. The storage 120 stores programs tobe executed by the CPU of the controller 110 and necessary data. Notethat data that is to be retained even after the power of the datatransmission device 100 is turned OFF is stored in non-volatile memorysuch as flash memory or the like.

The communicator 131 includes a wireless communication module and anantenna, and carries out data communication wirelessly with theanimation generation device 200. The data communication between thecommunicator 131 and the animation generation device 200 is not limitedto a wireless method. For example, the data communication may be carriedout using a wired interface such as a universal serial bus (USB) or thelike.

The input device 132 includes a press button switch or the like. In oneexample, the input device 132 receives input instructions such as “startmeasurement”, “transmit data”, and the like from the subject.

The output device 133 includes a light emitting diode (LED), a liquidcrystal display panel, an organic electro-luminescence (EL) displaypanel, or the like, and displays the operating state (power ON,measuring, transmitting data, and the like) of the data transmissiondevice 100. Additionally, the output device 133 includes an audio outputdevice such as a speaker or the like, and outputs, as audio information,information expressing the operating state or the like of the datatransmission device 100.

The sensor 134 includes an acceleration sensor, a gyro (angularvelocity) sensor, a global positioning system (GPS) receiver, and thelike. The sensor 134 detects the motion of the subject wearing the datatransmission device 100, the current position of the subject, and thelike. The sensor 134 sends, to the controller 110, acceleration datadetected by the acceleration sensor, angular velocity data detected bythe gyro sensor, time data and position data received by the GPSreceiver, and the like. These pieces of data that the sensor 134 sendsto the controller 110 are pieces of data for representing the exercisestate of the subject wearing the data transmission device 100 and, assuch, are collectively referred to hereinafter as “exercise data.” Notethat the data transmission device 100 (the sensor 134) may be worn on apart of the subject other than the waist (for example, on a wrist or anankle). Additionally, the data transmission device 100 may include aplurality of the sensor 134 such as, for example, a sensor 134 worn onthe waist, a sensor 134 worn on the wrist, a sensor 134 worn on theankle, or the like of the subject.

Next, the functions of the controller 110 are described. By executing aprogram stored in the storage 120, the controller 110 functions as anexercise data transmitter 111.

The exercise data transmitter 111 sends, via the communicator 131 to theanimation generation device 200, the exercise data detected by thesensor 134 (the acceleration data, the angular velocity data, the timedata, the position data, and the like that represents the motion of thesubject). The exercise data transmitter 111 may also send, as theexercise data to the animation generation device 200, movement distancedata calculated from the position data, speed data calculated from thetime data and the position data, acceleration data, and the like.

Next, the animation generation device 200 is described. In one example,the animation generation device 200 is a terminal device such as apersonal computer (PC), a smartphone, a tablet, or the like. Asillustrated in FIG. 1 , the animation generation device 200 includes acontroller 210, a storage 220, a communicator 231, an input device 232,and an output device 233. The controller 210, the storage 220, thecommunicator 231, the input device 232, and the output device 233 areconnected to each other via a bus line BL. The animation generationdevice 200 calculates, from the exercise data sent by the datatransmission device 100, values of a plurality of different types ofparameters as indexes that represent the exercise state of running,generates an animation representing the motion of running of thesubject, and presents the generated animation to the subject.

The controller 210 includes a CPU or the like. By executing a programstored in the storage 220, the controller 210 functions as variousconstituents that are described later (a parameter acquirer 211, ananimation generator 212, and a parameter estimator 213).

The storage 220 includes a ROM, a RAM, a flash memory, or the like. Thestorage 220 stores programs to be executed by the CPU of the controller210 and necessary data. Note that data that is to be retained even afterthe power of the animation generation device 200 is turned OFF is storedin non-volatile memory such as flash memory or the like. Additionally,the storage 220 stores a user database (DB) 221 and an exercise statestorage DB 222.

The user DB 221 is a database in which information related to thesubject of the data transmission device 100 is registered. Specifically,information expressing a user ID that uniquely identifies the subject, aname, a gender, a physique (height, weight, and the like), runninghistory, a best time, and the like is stored in the user DB 221 for eachsubject of the data transmission device 100.

The exercise state storage DB 222 is a database in which a plurality ofparameters representing the exercise state of running of the subjectmeasured to-date is stored. Specifically, as illustrated in FIG. 2 , aplurality of exercise parameter information in which the user ID of thesubject; as the parameters representing the exercise state of thesubject, various values of the speed, the pitch (steps per unit time),and the vertical movement of the movement/exercise of the subject; andinformation indicating a measurement date and time at which theseparameters are measured are associated is stored in the exercise statestorage DB 222. Note that it is empirically known that the variousvalues of the speed, the pitch, and the vertical movement that are theparameters representing the exercise state of the user have correlationwith each other and change in conjunction with each other due to changesin the exercise state of the subject. For example, when the speed of thesubject changes, the pitch and the vertical movement change inconjunction.

The communicator 231 includes a wireless communication module, anantenna, and the like, and carries out data communication wirelesslywith the data transmission device 100. The data communication betweenthe communicator 231 and the data transmission device 100 is not limitedto a wireless method. For example, the data communication may be carriedout using a wired interface such as USB or the like.

The input device 232 includes a switch, a touch panel, a keyboard, amouse, or the like. In one example, the input device 232 receives inputinstructions such as “generate animation”, “change parameters”, and thelike from the subject.

The output device 233 includes a liquid crystal display panel, anorganic EL display panel, or the like. In one example, the output device233 displays an animation generated in animation generation processing(described later), displays a screen for changing the parameters, andthe like. Additionally, the output device 233 may include an audiooutput device such as a speaker or the like, and may output audiorelated to the animation generated by the animation generationprocessing or the like.

Next, the functions of the controller 210 are described. By executing aprogram stored in the storage 220, the controller 210 functions aparameter acquirer 211, an animation generator 212, and a parameterestimator 213.

The parameter acquirer 211 acquires, via the communicator 231, theexercise data (the acceleration data, the angular velocity data, thetime data, the position data, the distance data, the speed data, and thelike) indicating the motion of the subject acquired from the datatransmission device 100. Moreover, the parameter acquirer 211calculates, from the acquired exercise data, a plurality of parameters(the speed, the pitch, and the vertical movement) that have correlationand that represent the exercise state of running of the subject, andregisters the calculated plurality of parameters in the exercise statestorage DB 222.

Note that the parameter acquirer 211 can use a known method to calculatethe various parameters from the exercise data. For example, theparameter acquirer 211 can use the method described in Japanese PatentNo. 6648439, the method described in Japanese Unexamined PatentApplication Publication No. 2019-216798, or the like.

For example, the parameter acquirer 211 can calculate the speed fromtime changes in the position data indicated by the exercise data.Additionally, the parameter acquirer 211 can obtain the waveform period(running period) of a vertical direction component of the accelerationindicated by the exercise data, and calculate the pitch from the fromthe running period. Moreover, the parameter acquirer 211 can integratethe vertical direction component of the acceleration indicated by theexercise data to calculate the vertical movement as the differencebetween the highest point and the lowest point of the position (theposition of the waist of the subject where the data transmission device100 is worn) from when one foot contacts the ground to when the otherfoot contacts the ground.

The animation generator 212 generates an animation representing thecorresponding motion of the subject on the basis of various values ofthe plurality of parameters specified as parameters to be animated.

When one value of the plurality of parameters specified as parameters tobe animated changes, the parameter estimator 213 generates, on the basisof the plurality of exercise parameter information stored in theexercise state storage DB 222, a model that defines the relationshipsamong the values of the plurality of parameters, and estimates thevalues of the parameters that are expected to change in conjunction.

Next, the processing executed by the animation generation device 200 isdescribed. Firstly, parameter acquisition processing executed by theanimation generation device 200 is described. The subject of the datatransmission device 100 wears the data transmission device 100, inputsan instruction of “start exercise data measurement” via the input device132 and, then, performs running or waking, for example, as exercise. Asa result, the sensor 134 of the data transmission device 100continuously measures, every predetermined amount of time (for example,every one second) the exercise data of the subject that is running orwalking. Thereafter, the subject that has finished running inputs aninstruction of “stop exercise data measurement” via the input device132. As a result, the exercise data transmitter 111 sends thecontinuously measured exercise data and the user ID of the subject tothe animation generation device 200. When the exercise data is receivedfrom the data transmission device 100, the parameter acquirer 211 of theanimation generation device 200 executes the parameter acquisitionprocessing illustrated in FIG. 3 .

Firstly, the parameter acquirer 211 divides the received exercise dataevery predetermined measurement time (for example, five minutes) (stepS101). For example, when the predetermined measurement time is fiveminutes and the received exercise data is exercise data of one hour, theexercise data is divided into 12 pieces in step S101. Note that theparameter acquirer 211 may determine a timing at which the speed changesgreater than or equal to a certain threshold from the transition of thespeed of the subject indicated by the received exercise data, and dividethe exercise data at that timing.

Next, the parameter acquirer 211 calculates, from the exercise data andfor each piece of the exercise data divided in step S101, values of thevarious parameters (the speed, the pitch, the vertical movement, and thelike) that represent the exercise state of the subject (step S102).

Next, the parameter acquirer 211 registers, in the exercise statestorage DB 222, the exercise parameter information that includes thecalculated various parameters (step S103). For example, when theexercise data is divided into 12 pieces in step S101, twelve pieces ofexercise parameter information are registered in the exercise statestorage DB 222. Note that the user ID included in the exercise parameterinformation is set to the user ID received together with the exercisedata. Then, the parameter acquisition processing is ended.

Note that, in the parameter acquisition processing described above, theanimation generation device 200 calculates the various parameters on thebasis of the exercise data received from the data transmission device100. However, a configuration is possible in which the data transmissiondevice 100 calculates the various parameters on the basis of theexercise data acquired by the sensor 134, and sends the calculatedvarious parameters to the animation generation device 200.

Next, animation generation processing executed by the animationgeneration device 200 is described. In this case, it is assumed that,prior to the animation generation processing, the parameter acquisitionprocessing described above is executed for a plurality of subjects, andthat a sufficient number of pieces of the exercise parameter information(for example, 100 pieces or more) is stored in the exercise statestorage DB 222. When the subject inputs an instruction of “generateanimation” via the input device 232 of the animation generation device200, the animation generator 212 starts the animation generationprocessing illustrated in FIG. 4 .

Firstly, the animation generator 212 receives, from the subject, valuesof the various parameters (the speed, the pitch, and the verticalmovement) to be animated (step S201). For example, the animationgenerator 212 receives, from the subject and via the input device 232, aselection of the exercise parameter information stored in the exercisestate storage DB 222, and receives the values of the various parametersincluded in the selected exercise parameter information as theparameters to be animated. Note that a configuration is possible inwhich the animation generation device 200 receives the values of thevarious parameters that are input directly by the subject via the inputdevice 232.

Next, the animation generator 212 creates, on the basis of the receivedvalues of the various parameters, an animation that represents theexercise state of running of the subject, and displays the createdanimation on the output device 233 as illustrated in FIG. 5 (step S202).On this screen, the created animation is displayed on the right side,and the values of the various parameters (the speed, the pitch, and thevertical movement) on which the animation is based and slide bars forchanging the value of each of the various parameters are displayed onthe left side.

Returning to FIG. 4 , when the subject desires to change one of theparameters on which the displayed animation is based, the subject moves,via the input device 232, the slide bar beside the parameter to bechanged left or right to a position that corresponds to the amount ofdesired change. When an operation for changing a parameter is received(step S203; Yes), the parameter estimator 213 executes parameterestimation processing for estimating the values of the other parametersthat change in conjunction with the changed parameter (step S204).

The parameter estimation processing is described in detail whilereferencing FIG. 6 . Note that, in the following description, theparameter for which the value is changed by an operation of the subjectis defined as a first parameter, and another parameter for which thevalue is estimated to change in conjunction with the first parameter isdefined as a second parameter. For example, when the subject performs,via the input device 232, an operation from the screen illustrated inFIG. 5 for changing the speed, the speed is the first parameter and thepitch or the vertical movement is the second parameter.

When the parameter estimation processing starts, the parameter estimator213 acquires the value of the first parameter and the value of thesecond parameter from before the change being performed by the operationof the subject (step S301).

Next, the parameter estimator 213 generates a model in which a thirdparameter is the input and a fourth parameter is the output (step S302).Here, the model is generated on the basis of all sets of the value ofthe third parameter, that is the same type as the first parameter, andthe value of the fourth parameter, that is the same type as the secondparameter, expressed by each piece of the plurality of exerciseparameter information stored in the exercise state storage DB 222. Inone example, this model corresponds to a function (regression equation)of a regression curve created by the least-square method. Note that aconfiguration is possible in which the parameter estimator 213 furthercalculates, as the model, a reliability interval expressing a range ofthe value of the fourth parameter that includes a certain percentage orgreater (for example, 60% or greater) of all of the sets of the thirdparameter and the fourth parameter.

For example, FIG. 7 illustrates a distribution of the sets of the thirdparameter, namely speed, and the fourth parameter, namely pitch,expressed by each piece of the plurality of exercise parameterinformation stored in the exercise state storage DB 222. In FIG. 7 ,each “※” symbol corresponds to a set of the speed and the pitch includedin one piece of the exercise parameter information. In step S302, amodel in which the speed is the input and the pitch is the output iscreated from this distribution by the least-square method. The solidline curve F in FIG. 7 is a regression curve corresponding to the modelcreated from this distribution. The two dashed line curves A and Brespectively indicate the upper limit and the lower limit of thereliability interval. 60% of the sets of the speed and the pitch denotedby the “※” symbol are included in the range between the dashed linecurves A and B. Note that the curves A and B that define the reliabilityinterval are calculated by a known method on the basis of therelationship between the curve F and the distribution of the sets of thespeed and the pitch.

Returning to FIG. 6 , next, the parameter estimator 213 inputs, into thecreated model, the value of the first parameter, from before the valueis changed, acquired in step S301, and derives a first reference valuethat is a value of the second parameter (step S303).

Next, the parameter estimator 213 inputs, into the created model, thevalue of the first parameter from after the value is changed by theoperation of the subject, and derives a second reference value that is avalue of the second parameter (step S304).

Next, the parameter estimator 213 estimates, on the basis of the valueof the unchanged second parameter acquired in step S301, the firstreference value, and the second reference value, a value of the secondparameter that changes in conjunction with the changed first parameter(step S305). Then, the parameter estimation processing is ended.

Next, an example of the processing of step S305 is described. In thiscase, it is assumed that the first parameter and the third parameter arespeed, the second parameter and the fourth parameter are pitch, and amodel such as the curve F illustrated in FIG. 8 is generated. Moreover,a case is considered in which, from a state in which the value of thespeed is V1 and the value of the pitch is P1, the subject performs anoperation for only changing the value of the speed to V2. In this case,the parameter estimator 213 estimates that the value of the pitchchanges so as to conform to the curve F. That is, the parameterestimator 213 estimates, on the basis of the following equation, thevalue P2 of the pitch that is the second parameter that changes inconjunction with the first parameter.

P2=S2+K*(P1−S1)

As illustrated in FIG. 8 , in this equation, P1 represents the value ofthe pitch corresponding to the speed V1 prior to the speed being changedto V2, S1 represents the first reference value, and S2 represents thesecond reference value. Additionally, in this equation, K is acoefficient set as desired in a range of 0 to 1. Note that K istypically set to 1.0. When it is desired to bring P2, which is theestimated value of the changed second parameter, close to the secondreference value S2, K is set to a value near 0.0.

Note that, when the reliability interval is also calculated as themodel, the parameter estimator 213 may estimate P2 while considering theratio (T2/T1) of the lengths of the reliability interval as illustratedin the following equation.

P2=S2+K*(P1−S1)*T2/T1

As illustrated in FIG. 8 , in this equation, T1 represents the length ofthe reliability interval at the unchanged speed V1. T2 represents thelength of the reliability interval at the changed speed V2. In oneexample, the equation of the curve A is expressed as a(v) and theequation of the curve B is expressed as b(v) as functions of the speedv. In this case, it is sufficient to calculate T1 and T2 on the basis ofthe following equations.

T1=a(V1)−b(V1)

T2=a(V2)−b(V2)

Here, an example is given in which the second parameter, namely pitch,is estimated when the first parameter, namely speed, is changed.However, a configuration is possible in which a set consisting of otherparameters for which the values have correlation are set as the firstparameter and the second parameter. For example, when the firstparameter and the second parameter are reversed and the first parameter,namely the pitch, is changed, it is possible to estimate the secondparameter, namely speed, by the same method.

In another example, there is correlation between the speed and thevertical movement and, as illustrated in FIG. 9 , a regression curve F′and a reliability interval (interval between a curve A′ and a curve B′)can be calculated as the model from the distribution of sets of thespeed and the vertical movement, this model can be used to estimate, inthe same manner, the second parameter, namely the vertical movement,when the first parameter, namely the speed, is changed. Alternatively,when the first parameter and the second parameter are reversed and thefirst parameter, namely the vertical movement, is changed, it ispossible to estimate the second parameter, namely the speed, in the samemanner.

In another example, there is correlation between the pitch and thevertical movement and, as illustrated in FIG. 10 , a regression curve F″and a reliability interval (interval between a curve A″ and a curve B″)can be calculated as the model from the distribution of sets of thepitch and the vertical movement, this model can be used to estimate, inthe same manner, the second parameter, namely the vertical movement,when the first parameter, namely the pitch, is changed. Alternatively,when the first parameter and the second parameter are reversed and thefirst parameter, namely the vertical movement, is changed, it ispossible to estimate the second parameter, namely the pitch, in the samemanner.

Returning to FIG. 4 , when the parameter estimation processing (stepS204) ends, the animation generator 212 creates an animation thatreflects the estimation results and updates the display (step S205). Inone example, it is assumed that the value of the speed us changed by anoperation of the subject and, in the parameter estimation processing,the values of the pitch and the vertical movement that change inconjunction with the value of the speed are estimated. In this case, theanimation generator 212 changes the values of the parameters and thepositions of the slider bars on the left side of the screen illustratedin FIG. 5 so as to become the value of the speed changed by the subject,and the value of the pitch and the value of the vertical movementestimated in the parameter estimation processing; creates an animationon the basis of these various values; and updates the animationdisplayed on the right side.

After the updating of the display in step S205 is ended or, when anoperation for changing a parameter is not received from the subject(step S203; No), the animation generator 212 determines whether aninstruction to end the animation generation processing (for example, aclick of the button “end” in the screen illustrated in FIG. 5 ) isreceived from the subject via the input device 232 (step S206). When theinstruction to end is not received (step S206; No), the processing ofstep S203 is executed. However, when the instruction to end is received(step S206; Yes), the animation generation processing is ended.

Thus, according to the present embodiment, a model (regression line) inwhich one parameter (for example, the speed) is the input and anotherparameter (for example, the pitch) is the output is generated from setsof parameters (for example, sets of the speed and the pitch), stored inthe exercise state storage DB 222, that represent the exercise states ofa plurality of subjects. Moreover, when the acquired value of oneparameter is changed, the manner in which the value of the otherparameter changes is estimated on the basis of the generated model. Thatis, according to the present embodiment, when one of the plurality ofparameters representing the exercise state changes or is assumed tochange, it is possible to estimate how the value of the other parameterwill change.

Additionally, according to the present embodiment, when the subjectperforms an operation or the like to change the value of one parameterof the plurality of parameters that represent the exercise state, themanner in which the value of the other parameter will change inconjunction with the change of the value of the one parameter isestimated, and an animation representing the exercise state is createdon the basis of the estimated value of the parameter. As such, naturalanimations can be created even when only the value of one parameterchanges.

Additionally, in the present embodiment, it is possible to calculate areliability interval in addition to the regression line as the model forestimating the parameter, and to estimate the parameter whileconsidering the lengths of the reliability intervals. As such, it ispossible to improve the accuracy of estimating parameters.

Modified Examples

Note that the present disclosure is not limited to the embodimentdescribed above, and various modifications of portions are possiblewithout departing from the spirit and scope of the present disclosure.

For example, in the embodiment described above, in the parameterestimation processing, the model is created from all of the exerciseparameter information stored in the exercise state storage DB 222.However, a configuration is possible in which, the model is created fromexercise parameter information that corresponds to the exercise state ofa subject that has the same attributes as the subject that is to beanimated. For example, in the parameter estimation processing, whenestimating parameters representing the exercise state of a subject thatis a female, the parameter estimator 213 may reference the user DB 221,extract, from the exercise state storage DB 222, exercise parameterinformation that corresponds to the exercise state of a female, andcreate the model from the extracted exercise parameter information.

In the embodiment described above, the speed, the pitch, and thevertical movement of the subject are described as examples of theparameters that represent the exercise state and that change inconjunction with each other. However, the parameters are not limitedthereto and other parameters may be set as the first parameter and thesecond parameter. For example, two parameters that have correlation,selected from, the speed, the pitch, and the vertical movement and,furthermore, a stride, a stride to height ratio, a vertical movement toheight ratio, a left-right movement, a forward-backward movement, aground contact time, a swing time, a ground contact time rate, adeceleration amount, a sink amount, a sink amount to height ratio, abraking time, a ground contact impact, a kick acceleration, a kick time,an amount of pelvic rotation, a stiffness, a stiffness to weight ratio,a ground contact angle, and a kicking angle may be set as the firstparameter and the second parameter. Note that, like the speed, thepitch, and the vertical movement, these parameters can be calculated byknown methods from exercise data acquired by the data transmissiondevice 100.

For example, the stride is the step width per step, and can becalculated by dividing the speed per minute by the pitch. The stride toheight ratio can be calculated by dividing the stride by the height ofthe subject. The vertical movement to height ratio can be calculated bydividing the vertical movement by the height of the subject.

The left-right movement is a left-right fluctuation range of theposition from when one foot contacts the ground to when the other footcontacts the ground, and can be calculated by integrating the left-rightdirection component of the acceleration indicated by the exercise data.The forward-backward movement is a forward-backward directionfluctuation range of the position from when one foot contacts the groundto when the other foot contacts the ground, and can be calculated byintegrating the forward-backward direction component of the accelerationindicated by the exercise data and subtracting the movement distance ataverage speed.

The ground contact time is an amount of time from when one foot contactsthe ground to when that foot leaves the ground, and can be calculated byidentifying, on the basis of the acceleration indicated by the exercisedata, the timings at which the ground is contacted and the ground isleft. The swing time is an amount of time from when one foot leaves theground to when that foot contacts the ground. The ground contact timerate can be calculated as follows:

ground contact time rate=ground contact time/(ground contact time+swingtime).

The deceleration amount can be calculated on the basis of theacceleration indicated by the exercise data, by integrating, for anamount corresponding to one cycle of one foot, the magnitude of anacceleration vector in the backward direction in a ground contactinterval. The sink amount is the difference between the position atground contact of one foot and a lowest point thereafter, and can becalculated by integrating, from the time of ground contact to the lowestpoint, the vertical direction component of the acceleration indicated bythe exercise data. The sink amount to height ratio can be calculated bydividing the sink amount by the height of the subject.

The braking time is the amount of time from ground contact to when theforward-backward component of the acceleration changes to the propulsiondirection, and can be calculated by identifying the timing of groundcontact and the timing at which the forward-backward component of theacceleration indicated by the exercise data changes to the propulsiondirection. The ground contact impact is the amount of impact when makingground contact, and can be expressed by the magnitudes immediately afterground contact of various components of an acceleration vector indicatedby the exercise data.

The kick acceleration is the magnitude of acceleration at propulsion,and can be expressed by the magnitude of the forward-backward directioncomponent of an acceleration vector indicated by the exercise data. Thekick time is the amount of time in which acceleration in the propulsiondirection is generated during a ground contact period, and can becalculated by measuring the amount of time that the forward-backwarddirection component of the acceleration indicated by the exercise datais generated. Alternatively, the kick time may be calculated on thebasis of the vertical movement component of the acceleration indicatedby the exercise data, by measuring the amount of time from the lowestpoint to when one foot leaves the ground.

The amount of pelvic rotation is an amount that the pelvis rotates fromwhen one foot contacts the ground to when that foot contacts the ground(two-step cycle), or from when one foot contacts the ground to when theother foot contacts the ground (one-step cycle), and can be calculatedon the basis of a rotation speed indicated by the exercise data. Thestiffness is a spring constant for a case in which the feet are regardedas springs, and can be calculated on the basis of a change in thevertical movement component of the acceleration indicated by theexercise data. The stiffness to weight ratio can be calculated bydividing the stiffness by the weight of the subject.

The ground contact angle is an angle between the speed vector at groundcontact and the horizontal plane or the ground surface, and the kickangle is an angle between the speed vector at ground leaving and thehorizontal plane or the ground surface. The ground contact angle and thekick angle can be calculated on the basis of the various directionalcomponents of the acceleration indicated by the exercise data.

In the embodiment described above, in the parameter estimationprocessing, the first reference value and the second reference value arederived by inputting the value of the first parameter into the createdmodel, namely a regression equation. However, the method for derivingthe first reference value and the second reference value from the modelis not limited thereto. For example, a configuration is possible inwhich the first reference value and the second reference value arederived by inputting, into the model, a value obtained by multiplyingthe value of the first parameter by a certain coefficient.

In the embodiment described above, it is described that the animationgeneration device 200 creates an animation that represents the exercisestate of running of the subject. However, the exercise state is notlimited to running. The present disclosure can be also be applied to ananimation generation device that creates an animation of a baseballpitching form, or the like, for example.

In the embodiment described above, an example is described of ananimation generation device 200 that creates an animation thatrepresents the exercise state of a subject that is a human. However, thesubject of the animation to be created is not limited to a human, andthe present disclosure can be applied to an animation generation device200 that creates an animation that represents the exercise state of asubject other than a human. For example, the present disclosure can bealso be applied to an animation generation device that generates ananimation that represents a running state of a race horse, an operatingstate of a robot, or the like.

The present disclosure is not limited to the animation generation device200. For example, the present disclosure can be also be applied to aninformation processing device that executes only the parameterestimation processing without creating an animation, or the like.

In the embodiment described above, in the parameter estimationprocessing, the animation generation device 200 generates a model forestimating a parameter. However, a configuration is possible in which amodel is generated in advance by an external server or the like and, inthe parameter estimation processing, the animation generation device 200estimates the parameter on the basis of the model acquired from theexternal server or the like. With such a configuration, the processingfor generating the model by the animation generation device 200 can beeliminated and, as a result, the load applied to the animationgeneration device 200 can be reduced and the processing time of theparameter estimation processing can be shortened.

The various functions of the animation generation device 200 can beimplemented by a computer such as a typical personal computer (PC) orthe like. Specifically, in the embodiment described above, an examplesis described in which programs of the animation generation processing,performed by the animation generation device 200, are stored in advancein the ROM of the storage 220. However, a computer may be configuredthat is capable of realizing these various features by storing anddistributing the programs on a non-transitory computer-readablerecording medium such as a compact disc read-only memory (CD-ROM), adigital versatile disc (DVD), a magneto-optical disc (MO), a memorycard, and universal serial bus (USB) memory, and reading out andinstalling these programs on the computer.

The foregoing describes some example embodiments for explanatorypurposes. Although the foregoing discussion has presented specificembodiments, persons skilled in the art will recognize that changes maybe made in form and detail without departing from the broader spirit andscope of the invention. Accordingly, the specification and drawings areto be regarded in an illustrative rather than a restrictive sense. Thisdetailed description, therefore, is not to be taken in a limiting sense,and the scope of the invention is defined only by the included claims,along with the full range of equivalents to which such claims areentitled.

What is claimed is:
 1. An information processing device comprising: amemory that stores a program; and a processor that executes the program,wherein the processor is configured to acquire, from exercise datarepresenting an exercise state of a subject, exercise parameterinformation including a plurality of parameters that represent theexercise state of the subject and have a correlation with each other,and when an animation representing a motion of the subject based on theacquired exercise parameter information is displayed and then anoperation for changing a value of a first parameter of the plurality ofparameters is received, generate an animation reflecting at least thefirst parameter for which the value is changed and a second parameter ofthe plurality of parameters, a value of the second parameter beingchanged in conjunction with the value of the first parameter.
 2. Theinformation processing device according to claim 1, wherein theprocessor is further configured to generate a model defining arelationship between the value of the first parameter and the value ofthe second parameter, input, to the generated model, a value of thefirst parameter before receiving the operation for changing and a valueof the first parameter after receiving the operation for changing,derive, from the input values of the first parameter, respectivereference values of the second parameter, and estimate, based on thederived reference values, the value of the second parameter.
 3. Theinformation processing device according to claim 2, wherein the modelgenerated by the processor is a regression curve created from adistribution of a plurality of sets of the value of the first parameterand the value of the second parameter by a least-square method, the setsbeing previously stored in the memory.
 4. The information processingdevice according to claim 3, wherein the regression curve furtherincludes a regression curve indicating a reliability interval.
 5. Theinformation processing device according to claim 1, further comprising:a display, wherein the processor is configured to display the generatedanimation on the display.
 6. An information processing devicecomprising: a memory that stores a program; a display; and a processorthat executes the program, wherein the processor is configured toacquire, from operation data representing an operating state of a mobileobject, a plurality of pieces of operation parameter informationrepresenting operation of the mobile object, display, based on theplurality of pieces of operation parameter information, a firstanimation representing a motion of the mobile object on the display, andwhen the first animation is displayed and then an operation for changinga value of a first parameter of the plurality of pieces of operationparameter information is received, display, on the display, a secondanimation reflecting the first parameter for which the value is changedand a second parameter for which a value is changed in conjunction withthe value of the first parameter.
 7. An information processing methodexecuted by an information processing device, the method comprising:acquiring, from exercise data representing an exercise state of asubject, exercise parameter information including a plurality ofparameters that represent the exercise state of the subject and have acorrelation with each other, and when an animation representing a motionof the subject based on the acquired exercise parameter information isdisplayed and then an operation for changing a value of a firstparameter of the plurality of parameters is received, generating ananimation reflecting at least the first parameter for which the value ischanged and a second parameter of the plurality of parameters, a valueof the second parameter being changed in conjunction with the value ofthe first parameter.
 8. The information processing method according toclaim 7, further comprising: generating a model defining a relationshipbetween the value of the first parameter and the value of the secondparameter, inputting, to the generated model, a value of the firstparameter before receiving the operation for changing and a value of thefirst parameter after receiving the operation for changing, deriving,from the input values of the first parameter, respective referencevalues of the second parameter, and estimating, based on the derivedreference values, the value of the second parameter.
 9. The informationprocessing method according to claim 8, wherein the model generated bythe processor is a regression curve created from a distribution of aplurality of sets of the value of the first parameter and the value ofthe second parameter by a least-square method, the sets being previouslystored in the memory.
 10. The information processing method according toclaim 8, wherein the regression curve further includes a regressioncurve indicating a reliability interval.
 11. The information processingmethod according to claim 7, further comprising: displaying thegenerated animation on the display.
 12. A non-transitorycomputer-readable recording medium storing a program for causing acomputer to execute: acquiring, from exercise data representing anexercise state of a subject, exercise parameter information including aplurality of parameters that represent the exercise state of the subjectand have a correlation with each other, and when an animationrepresenting a motion of the subject based on the acquired exerciseparameter information is displayed and then an operation for changing avalue of a first parameter of the plurality of parameters is received,generating an animation reflecting at least the first parameter forwhich the value is changed and a second parameter of the plurality ofparameters, a value of the second parameter being changed in conjunctionwith the value of the first parameter.